The role of structural and network-level factors have increasingly been recognized to be important in understanding the spread of HIV infection, as researchers have found that individual-level characteristics are not sufficient to explain observed HIV epidemics. South Africa has a significant burden of HIV; with HIV prevalence estimated to be 18.8% among adults aged 15-49, and the largest absolute number of people living with HIV worldwide. A recent trial found that early HIV treatment prevents onward transmission, and current recommendations include both regular HIV testing and early treatment. Mathematical models have simulated the impact of scaling up a test-and-treat program to estimate the elimination of HIV in South Africa. These models primarily assume that individuals who are missed by test-and-treat are missed at random and do not incorporate heterogeneity in structural factors or in sexual behavior by community. The relationship between area-level structural factors and HIV infection and testing history has not been consistently characterized before in a nationally representative survey of South Africans. Similarly, spatial clustering of sexual behavior has not been well described in South Africa. Understanding the impact of structural and network-level drivers of HIV is important for targeting HIV prevention and treatment interventions. In 2012, the fourth South African National HIV Behavior and Health Survey (SABBSM IV), a nationally representative household-based multistage cluster sample survey of all South Africans, was conducted. SABSSM IV will be an ideal study to understand the impact of structural and network characteristics on the South African HIV epidemic as well as understanding the role of these factors in mathematical modeling of HIV treatment as prevention interventions. We will assess the association of structural factors (including area-level education, income, unemployment and migration) with individual-level HIV status and HIV testing history. We will next assess spatial clustering of sexual behavior among South African adults. Finally, we will develop a mathematical network model of HIV transmission in simulated communities in South Africa that takes into account differential HIV testing by structural factors, HIV treatment by demographic characteristics, and heterogeneity in sexual behavior across communities in assessing the impact of HIV treatment as prevention interventions. These aims can inform HIV prevention interventions and policy by assessing the impact of structural- and network-level factors on HIV transmission as well as better understanding of the role of differential HIV testing, treatment and sexual behavior on the impact of interventions. The proposed work will: 1) inform combination HIV prevention interventions in South Africa; 2) develop the applicant's expertise in mathematical modeling of HIV transmission, spatial statistics, and multilevel modeling; and 3) provide a basis for further research, particularly the expansion of the proposed mathematical model to additional populations and settings in post-doctoral work.